We are developing the Indicator Cell Assay Platform (iCAP), a broadly applicable and inexpensive blood- based assay that can be used for early detection of disease, disease stage stratification, prognosis and response to therapeutic intervention for a variety of diseases. The iCAP uses cultured cells as biosensors, capitalizing on the ability of cells to respond differently to signals present in the serum (or other biofluid) from normal or diseased subjects with exquisite sensitivity, as opposed to traditional assays that rely on direct detection of molecules in blood. Developing the iCAP involves exposing cultured cells to serum from normal or diseased subjects, measuring a global differential response pattern, and using it to build a reliable disease classifier comprised of a small number of features. Deploying the iCAP involves measuring only expression genes that are features of the disease classifier using cost-effective tools. Indicator cells are chosen based on the disease application, and those typically selected have known relationships to the disease pathology. The iCAP can overcome barriers to blood-based diagnostics like broad dynamic range of blood components, low abundance of specific markers, and high levels of noise. The focus of this proposal is to initiate development of the iCAP for blood-based diagnosis of lung cancer (LC). Blood biomarkers of LC are desperately needed for use in combination with existing imaging tools to improve diagnostic accuracy. Our long-term goal is to develop a blood-based assay for clinical use on patients that have indeterminate pulmonary nodules identified by imaging to distinguish those with LC from those with benign nodules. This assay will help patients with benign nodules avoid invasive biopsy and focus further diagnostic tests on those with LC. For this Phase I study, we propose to develop an assay to reliably distinguish LC from benign nodules from patient serum samples. Specifically, we aim to 1) identify optimal indicator cells for the assay, 2) develop an iCAP-based classifier to reliably distinguish patients with lung cancer from those with benign nodules using patient serum, and 3) validate the assay by analyzing new serum samples. If we successfully achieve robust classification with >90% blind predictive accuracy and 70% sensitivity and specificity, the assay will be optimized and validated with larger cohorts in a Phase II study.